Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "1992"
  #_source: array:26 [
    "id" => "1992"
    "slug" => "main-effects-and-interactions-in-mixed-and-incomplete-data-frames"
    "yearMonth" => "2019-05"
    "year" => "2019"
    "title" => "Main Effects and Interactions in Mixed and Incomplete Data Frames"
    "description" => "ROBIN, G., KLOPP, O., JOSSE, J., MOULINES, E. et TIBSHIRANI, R. (2019). Main Effects and Interactions in Mixed and Incomplete Data Frames. <i>Journal of the American Statistical Association</i>, 115(531), pp. 1292-1303."
    "authors" => array:5 [
      0 => array:3 [
        "name" => "KLOPP Olga"
        "bid" => "B00732676"
        "slug" => "klopp-olga"
      ]
      1 => array:1 [
        "name" => "ROBIN Geneviève"
      ]
      2 => array:1 [
        "name" => "JOSSE J."
      ]
      3 => array:1 [
        "name" => "MOULINES E."
      ]
      4 => array:1 [
        "name" => "TIBSHIRANI R."
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:3 [
      0 => "Heterogeneous data"
      1 => "Low-rank matrix completion"
      2 => "Missing values"
    ]
    "updatedAt" => "2021-09-24 10:33:27"
    "publicationUrl" => "https://www.tandfonline.com/doi/abs/10.1080/01621459.2019.1623041?journalCode=uasa20"
    "publicationInfo" => array:3 [
      "pages" => "1292-1303"
      "volume" => "115"
      "number" => "531"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => """
        A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abundance data in ecology to recommender systems. In many cases, an MDF exhibits simultaneously main effects, such as row, column or group effects and interactions, for which a low-rank model has often been suggested. Although the literature on low-rank approximations is very substantial, with few exceptions, existing methods do not allow to incorporate main effects and interactions while providing statistical guarantees. The present work fills this gap. We propose an estimation method which allows to recover\n
        \n
        simultaneously the main effects and the interactions. We show that our method is near optimal under conditions which are met in our targeted applications. We also propose an optimization algorithm which provably converges to an optimal solution. Numerical experiments reveal that our method, mimi, performs well when the main effects are sparse and the interaction matrix has low-rank. We also show that mimi compares favorably to existing methods, in particular when the main effects are significantly large compared to the interactions, and when the proportion of missing entries is large. The method is available as an R package on the Comprehensive R Archive Network.
        """
      "en" => """
        A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abundance data in ecology to recommender systems. In many cases, an MDF exhibits simultaneously main effects, such as row, column or group effects and interactions, for which a low-rank model has often been suggested. Although the literature on low-rank approximations is very substantial, with few exceptions, existing methods do not allow to incorporate main effects and interactions while providing statistical guarantees. The present work fills this gap. We propose an estimation method which allows to recover\n
        \n
        simultaneously the main effects and the interactions. We show that our method is near optimal under conditions which are met in our targeted applications. We also propose an optimization algorithm which provably converges to an optimal solution. Numerical experiments reveal that our method, mimi, performs well when the main effects are sparse and the interaction matrix has low-rank. We also show that mimi compares favorably to existing methods, in particular when the main effects are significantly large compared to the interactions, and when the proportion of missing entries is large. The method is available as an R package on the Comprehensive R Archive Network.
        """
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-22T09:21:44.000Z"
    "docTitle" => "Main Effects and Interactions in Mixed and Incomplete Data Frames"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/klopp-olga">KLOPP Olga</a>, ROBIN Geneviève, JOSSE J., MOULINES E., TIBSHIRANI R."
    "docDescription" => "<span class="document-property-authors">KLOPP Olga, ROBIN Geneviève, JOSSE J., MOULINES E., TIBSHIRANI R.</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2019</span>"
    "keywordList" => "<a href="#">Heterogeneous data</a>, <a href="#">Low-rank matrix completion</a>, <a href="#">Missing values</a>"
    "docPreview" => "<b>Main Effects and Interactions in Mixed and Incomplete Data Frames</b><br><span>2019-05 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://www.tandfonline.com/doi/abs/10.1080/01621459.2019.1623041?journalCode=uasa20" target="_blank">Main Effects and Interactions in Mixed and Incomplete Data Frames</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.660635
  +"parent": null
}